Implementing AI in Healthcare: Start Small for Real Results
- The challenges facing healthcare organizations face
- Starting with smaller projects still yield big results
- Identifying Areas in RCM for AI Implementation
It is no secret that Artificial Intelligence has immense potential to drastically change and improve healthcare. What many healthcare organizations and their vendors are struggling with is not their vision of AI in healthcare, it is how to implement this technology for tangible results. Often the vision of many leaders in healthcare and their goals require multi-year projects that further require a large amount of capital and resources.
The Challenges
In an article at Health IT Today, Ori Geva, Co-Founder and President of Medial EarlySign, lays out the challenges of implementing AI in healthcare:
This is a common vision that healthcare leaders -- and by nature in any industry -- find extremely difficult to achieve. The article provides the following explanation:
These projects tend to have a high bar and lofty goals, requiring large sums of money to execute, and multiple adjustments and reassessments throughout the process. The scope of such projects is often so large that it becomes difficult, or even impossible to attain tangible results in a timely and cost-efficient manner.
Start Small for Big Results
It is important for healthcare organizations and RCM servicers to identify areas of focus where AI can provide realistic and tangible results. Once these areas are identified, it is equally important to outline the resources needed for each project. While many healthcare leaders have lofty visions for AI, Geva recommends starting with smaller projects:
The benefits of pursuing smaller projects are clear, allowing models to be tested and tuned with existing systems to optimize for increased scale in the future. Dividing the challenges into smaller baskets and implementing them on a project-by-project basis can enable us to work smarter, learn faster, and affect real change. It’s not just about technology and who has the greatest algorithm, it’s about finding a more productive solution. Ultimately, it’s about bringing AI down to earth.
Identifying Areas in RCM for AI Implementation
One area that healthcare organizations and RCM servicers have identified for implementation of AI is remittance processing. There remains an abundance of paper EOBs and remits currently being manually processed whether internally or through outsourced BPOs. In additional to inefficiency, the COVID-19 pandemic exposed the vulnerability of manual processing as well. As many countries moved its workforce to remote environments and closed physical offices, the processing of paper EOBs and remits was brought to a standstill due the nature of these documents, often containing PHI. This has resulted in delays with which healthcare organizations and RCM servicers are still trying to catch up.
For adopters of AI technologies for remit processing, the electronification of these paper-based documents allowed for minimum disruptions in the process. Rather than relying on manual posting, AI extracts the data from the paper-based remits and EOBs to electronify these documents into postable EDI 835 files. In addition to streamlining the payments processing, electronification with AI minimizes errors cause by manual posting, ensuring more accurate data in the system. Check out the video below to learn more about electronification of payments processing in healthcare.
